ABSTRACT: LogicInk will design and develop a discrete, low-cost, wearable alcohol-sensor that works via detection of ethanol in sweat and requires no power supply. The wearable device will let users better understand the impact of their drinking habits on their body, as reflected by alcohol concentration levels over time. These discrete, electronics-free wearables that resemble temporary tattoos will change color based on alcohol content in sweat. The color changes will be directly readable by the wearer requiring no additional equipment. In parallel, we will develop computer vision algorithms that will detect, record, and interpret the wearable?s colors in much greater detail for the user. These algorithms will serve as the basis for the development of a personalized mobile application that can track a users? alcohol consumption over time. Approximately 6.8% of adults and 20% of college students in the US have alcohol use disorder (AUD) and the economic burden of alcohol misuse is $249.0 billion. An alcohol-impaired driver is a factor in over a third of all traffic-related deaths. A significant problem with alcohol consumption is that drinkers? recall of the amount consumed is poor, as is judgment regarding the degree of impairment. The LogicInk biosensor utilizes a novel concentration metric that grants users a better understanding of the impact of their drinking habits on their body, as reflected by approximated transdermal alcohol levels. Additionally, the optional coupling to a mobile phone application allows users to track and analyze values over time. A simple, accurate, and inexpensive method to approximate cumulative alcohol consumption is a promising strategy to raise alcohol consumption awareness across a wide user demographic. The concentration of ethanol in sweat is highly correlated with blood alcohol concentrations (BACs) and has been used in many transdermal BAC devices. We will test the hypothesis that the biosensor can approximate blood alcohol levels and the amount of alcohol consumed since applying the sensor. We will have two aims in this proposal: Aim 1, Design and characterization of a cumulative alcohol biosensor, and Aim 2, Development and training of computer vision algorithms that will be used to track cumulative alcohol levels.